Overview

Dataset statistics

Number of variables49
Number of observations347469
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory129.9 MiB
Average record size in memory392.0 B

Variable types

Numeric10
Categorical39

Alerts

geo_level_1_id is highly correlated with geo_level_2_id and 1 other fieldsHigh correlation
geo_level_2_id is highly correlated with geo_level_1_id and 1 other fieldsHigh correlation
geo_level_3_id is highly correlated with geo_level_1_id and 1 other fieldsHigh correlation
count_floors_pre_eq is highly correlated with height_percentage and 1 other fieldsHigh correlation
height_percentage is highly correlated with count_floors_pre_eq and 1 other fieldsHigh correlation
land_surface_condition_1 is highly correlated with land_surface_condition_3High correlation
land_surface_condition_3 is highly correlated with land_surface_condition_1High correlation
foundation_type is highly correlated with roof_type_3 and 1 other fieldsHigh correlation
roof_type_1 is highly correlated with roof_type_2High correlation
roof_type_2 is highly correlated with roof_type_1High correlation
roof_type_3 is highly correlated with foundation_type and 2 other fieldsHigh correlation
ground_floor_type is highly correlated with roof_type_3High correlation
other_floor_type_1 is highly correlated with other_floor_type_2 and 1 other fieldsHigh correlation
other_floor_type_2 is highly correlated with other_floor_type_1High correlation
other_floor_type_3 is highly correlated with count_floors_pre_eq and 2 other fieldsHigh correlation
other_floor_type_4 is highly correlated with roof_type_3High correlation
position_1 is highly correlated with position_2High correlation
position_2 is highly correlated with position_1High correlation
has_superstructure_mud_mortar_stone is highly correlated with foundation_typeHigh correlation
has_secondary_use is highly correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
geo_level_1_id is highly correlated with geo_level_2_id and 1 other fieldsHigh correlation
geo_level_2_id is highly correlated with geo_level_1_id and 1 other fieldsHigh correlation
geo_level_3_id is highly correlated with geo_level_1_id and 1 other fieldsHigh correlation
count_floors_pre_eq is highly correlated with height_percentage and 1 other fieldsHigh correlation
height_percentage is highly correlated with count_floors_pre_eq and 1 other fieldsHigh correlation
land_surface_condition_1 is highly correlated with land_surface_condition_3High correlation
land_surface_condition_3 is highly correlated with land_surface_condition_1High correlation
foundation_type is highly correlated with roof_type_3 and 3 other fieldsHigh correlation
roof_type_1 is highly correlated with roof_type_2High correlation
roof_type_2 is highly correlated with roof_type_1High correlation
roof_type_3 is highly correlated with foundation_type and 2 other fieldsHigh correlation
ground_floor_type is highly correlated with foundation_type and 3 other fieldsHigh correlation
other_floor_type_1 is highly correlated with other_floor_type_2 and 1 other fieldsHigh correlation
other_floor_type_2 is highly correlated with other_floor_type_1High correlation
other_floor_type_3 is highly correlated with count_floors_pre_eq and 2 other fieldsHigh correlation
other_floor_type_4 is highly correlated with foundation_type and 2 other fieldsHigh correlation
position_1 is highly correlated with position_2High correlation
position_2 is highly correlated with position_1High correlation
has_superstructure_mud_mortar_stone is highly correlated with foundation_typeHigh correlation
has_superstructure_cement_mortar_brick is highly correlated with ground_floor_typeHigh correlation
has_secondary_use is highly correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
geo_level_1_id is highly correlated with geo_level_2_id and 1 other fieldsHigh correlation
geo_level_2_id is highly correlated with geo_level_1_id and 1 other fieldsHigh correlation
geo_level_3_id is highly correlated with geo_level_1_id and 1 other fieldsHigh correlation
count_floors_pre_eq is highly correlated with height_percentage and 1 other fieldsHigh correlation
height_percentage is highly correlated with count_floors_pre_eq and 1 other fieldsHigh correlation
land_surface_condition_1 is highly correlated with land_surface_condition_3High correlation
land_surface_condition_3 is highly correlated with land_surface_condition_1High correlation
foundation_type is highly correlated with roof_type_3 and 1 other fieldsHigh correlation
roof_type_1 is highly correlated with roof_type_2High correlation
roof_type_2 is highly correlated with roof_type_1High correlation
roof_type_3 is highly correlated with foundation_type and 2 other fieldsHigh correlation
ground_floor_type is highly correlated with roof_type_3High correlation
other_floor_type_1 is highly correlated with other_floor_type_2 and 1 other fieldsHigh correlation
other_floor_type_2 is highly correlated with other_floor_type_1High correlation
other_floor_type_3 is highly correlated with count_floors_pre_eq and 2 other fieldsHigh correlation
other_floor_type_4 is highly correlated with roof_type_3High correlation
position_1 is highly correlated with position_2High correlation
position_2 is highly correlated with position_1High correlation
has_superstructure_mud_mortar_stone is highly correlated with foundation_typeHigh correlation
has_secondary_use is highly correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
has_secondary_use is highly correlated with has_secondary_use_hotel and 1 other fieldsHigh correlation
other_floor_type_4 is highly correlated with ground_floor_type and 2 other fieldsHigh correlation
ground_floor_type is highly correlated with other_floor_type_4 and 2 other fieldsHigh correlation
position_1 is highly correlated with position_2High correlation
has_superstructure_rc_engineered is highly correlated with foundation_typeHigh correlation
roof_type_1 is highly correlated with roof_type_2High correlation
has_superstructure_cement_mortar_brick is highly correlated with ground_floor_type and 1 other fieldsHigh correlation
other_floor_type_2 is highly correlated with other_floor_type_1High correlation
foundation_type is highly correlated with other_floor_type_4 and 5 other fieldsHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
roof_type_3 is highly correlated with other_floor_type_4 and 2 other fieldsHigh correlation
other_floor_type_1 is highly correlated with other_floor_type_2 and 1 other fieldsHigh correlation
has_superstructure_rc_non_engineered is highly correlated with foundation_typeHigh correlation
roof_type_2 is highly correlated with roof_type_1High correlation
land_surface_condition_1 is highly correlated with land_surface_condition_3High correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
has_superstructure_mud_mortar_stone is highly correlated with foundation_typeHigh correlation
position_2 is highly correlated with position_1High correlation
land_surface_condition_3 is highly correlated with land_surface_condition_1High correlation
other_floor_type_3 is highly correlated with other_floor_type_1High correlation
geo_level_1_id is highly correlated with geo_level_2_id and 1 other fieldsHigh correlation
geo_level_2_id is highly correlated with geo_level_1_id and 1 other fieldsHigh correlation
geo_level_3_id is highly correlated with geo_level_1_id and 1 other fieldsHigh correlation
count_floors_pre_eq is highly correlated with height_percentage and 2 other fieldsHigh correlation
height_percentage is highly correlated with count_floors_pre_eq and 1 other fieldsHigh correlation
land_surface_condition_1 is highly correlated with land_surface_condition_2 and 1 other fieldsHigh correlation
land_surface_condition_2 is highly correlated with land_surface_condition_1High correlation
land_surface_condition_3 is highly correlated with land_surface_condition_1High correlation
foundation_type is highly correlated with roof_type_3 and 2 other fieldsHigh correlation
roof_type_1 is highly correlated with roof_type_2 and 1 other fieldsHigh correlation
roof_type_2 is highly correlated with roof_type_1High correlation
roof_type_3 is highly correlated with foundation_type and 7 other fieldsHigh correlation
ground_floor_type is highly correlated with foundation_type and 1 other fieldsHigh correlation
other_floor_type_1 is highly correlated with count_floors_pre_eq and 2 other fieldsHigh correlation
other_floor_type_2 is highly correlated with other_floor_type_1High correlation
other_floor_type_3 is highly correlated with count_floors_pre_eq and 2 other fieldsHigh correlation
other_floor_type_4 is highly correlated with foundation_type and 5 other fieldsHigh correlation
position_1 is highly correlated with position_2High correlation
position_2 is highly correlated with position_1 and 1 other fieldsHigh correlation
position_3 is highly correlated with position_2High correlation
has_superstructure_mud_mortar_stone is highly correlated with roof_type_3 and 3 other fieldsHigh correlation
has_superstructure_mud_mortar_brick is highly correlated with has_superstructure_mud_mortar_stoneHigh correlation
has_superstructure_cement_mortar_brick is highly correlated with roof_type_3 and 2 other fieldsHigh correlation
has_superstructure_timber is highly correlated with has_superstructure_bambooHigh correlation
has_superstructure_bamboo is highly correlated with has_superstructure_timberHigh correlation
has_superstructure_rc_non_engineered is highly correlated with roof_type_3 and 1 other fieldsHigh correlation
has_superstructure_rc_engineered is highly correlated with roof_type_3 and 1 other fieldsHigh correlation
has_secondary_use is highly correlated with has_secondary_use_agriculture and 1 other fieldsHigh correlation
has_secondary_use_agriculture is highly correlated with has_secondary_useHigh correlation
has_secondary_use_hotel is highly correlated with has_secondary_useHigh correlation
building_id has unique values Unique
age has 34725 (10.0%) zeros Zeros
count_families has 27937 (8.0%) zeros Zeros

Reproduction

Analysis started2022-04-30 11:02:59.782335
Analysis finished2022-04-30 11:05:40.165238
Duration2 minutes and 40.38 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

building_id
Real number (ℝ≥0)

UNIQUE

Distinct347469
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525913.5838
Minimum4
Maximum1052934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:40.247507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile52200.8
Q1261999
median526071
Q3789588
95-th percentile1000694
Maximum1052934
Range1052930
Interquartile range (IQR)527589

Descriptive statistics

Standard deviation304354.4791
Coefficient of variation (CV)0.5787157595
Kurtosis-1.201737909
Mean525913.5838
Median Absolute Deviation (MAD)263777
Skewness0.001061379559
Sum1.827386671 × 1011
Variance9.263164894 × 1010
MonotonicityNot monotonic
2022-04-30T13:05:40.362246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8029061
 
< 0.1%
488391
 
< 0.1%
8461601
 
< 0.1%
6030421
 
< 0.1%
2780561
 
< 0.1%
5572381
 
< 0.1%
2330081
 
< 0.1%
7513161
 
< 0.1%
6792651
 
< 0.1%
4639521
 
< 0.1%
Other values (347459)347459
> 99.9%
ValueCountFrequency (%)
41
< 0.1%
71
< 0.1%
81
< 0.1%
121
< 0.1%
131
< 0.1%
161
< 0.1%
171
< 0.1%
251
< 0.1%
281
< 0.1%
311
< 0.1%
ValueCountFrequency (%)
10529341
< 0.1%
10529311
< 0.1%
10529291
< 0.1%
10529261
< 0.1%
10529231
< 0.1%
10529211
< 0.1%
10529151
< 0.1%
10529111
< 0.1%
10529091
< 0.1%
10529081
< 0.1%

geo_level_1_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.239031638
Minimum1.73088658
Maximum2.794480356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:40.472366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.73088658
5-th percentile1.73088658
Q12.026023244
median2.171745152
Q32.44645707
95-th percentile2.794480356
Maximum2.794480356
Range1.063593776
Interquartile range (IQR)0.4204338258

Descriptive statistics

Standard deviation0.2875388683
Coefficient of variation (CV)0.1284210832
Kurtosis-0.5526004862
Mean2.239031638
Median Absolute Deviation (MAD)0.1707854595
Skewness0.1379842714
Sum777994.0841
Variance0.08267860079
MonotonicityNot monotonic
2022-04-30T13:05:40.576280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2.16172429432485
 
9.3%
1.7308865830002
 
8.6%
2.34195389329399
 
8.5%
2.79448035629265
 
8.4%
2.29772559825565
 
7.4%
2.48527253725465
 
7.3%
1.92646375522761
 
6.6%
2.5633689319944
 
5.7%
2.16213618919462
 
5.6%
2.4464570716786
 
4.8%
Other values (21)96335
27.7%
ValueCountFrequency (%)
1.7308865830002
8.6%
1.8946564891737
 
0.5%
1.9197026023579
 
1.0%
1.92646375522761
6.6%
1.9376561212846
3.7%
1.9607552763588
 
1.0%
2.0009596938358
 
2.4%
2.0204765453595
 
1.0%
2.0260232445213
 
1.5%
2.0495915992319
 
0.7%
ValueCountFrequency (%)
2.79448035629265
8.4%
2.7083725314200
 
1.2%
2.5633689319944
5.7%
2.48527253725465
7.3%
2.4464570716786
4.8%
2.40754717349
 
0.1%
2.34195389329399
8.5%
2.33771289510895
 
3.1%
2.3315649879995
 
2.9%
2.29772559825565
7.4%

geo_level_2_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1024
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.238639886
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:40.689564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.630630631
Q12.017964072
median2.210382514
Q32.47983871
95-th percentile2.877300613
Maximum3
Range2
Interquartile range (IQR)0.4618746378

Descriptive statistics

Standard deviation0.3579539089
Coefficient of variation (CV)0.1598979412
Kurtosis-0.3556521627
Mean2.238639886
Median Absolute Deviation (MAD)0.2237753708
Skewness0.02249890571
Sum777857.9624
Variance0.1281310009
MonotonicityNot monotonic
2022-04-30T13:05:40.811679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5321941565367
 
1.5%
1.8952380953317
 
1.0%
2.2855769232805
 
0.8%
2.2186274512675
 
0.8%
1.9393779652548
 
0.7%
2.518752343
 
0.7%
2.1821839082310
 
0.7%
1.8362676062249
 
0.6%
2.5409738722217
 
0.6%
2.2656826572129
 
0.6%
Other values (1014)319509
92.0%
ValueCountFrequency (%)
18
 
< 0.1%
1.218181818157
< 0.1%
1.22641509476
 
< 0.1%
1.231481481148
< 0.1%
1.2520
 
< 0.1%
1.282868526355
0.1%
1.30434782634
 
< 0.1%
1.325105
 
< 0.1%
1.3333333333
 
< 0.1%
1.35483871234
0.1%
ValueCountFrequency (%)
362
 
< 0.1%
2.990291262282
0.1%
2.984771574254
 
0.1%
2.977443609177
 
0.1%
2.97706422275
0.1%
2.976878613457
0.1%
2.965384615338
0.1%
2.958525346289
0.1%
2.953907816651
0.2%
2.951612903256
 
0.1%

geo_level_3_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1634
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.238719798
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:40.948603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.557446809
Q12
median2.2
Q32.5
95-th percentile2.944444444
Maximum3
Range2
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3992658962
Coefficient of variation (CV)0.1783456316
Kurtosis-0.1751926784
Mean2.238719798
Median Absolute Deviation (MAD)0.2310344828
Skewness-0.0501692177
Sum777885.7295
Variance0.1594132559
MonotonicityNot monotonic
2022-04-30T13:05:41.206543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221349
 
6.1%
39985
 
2.9%
2.53989
 
1.1%
2.3333333333264
 
0.9%
2.252966
 
0.9%
2.22851
 
0.8%
2.1666666672850
 
0.8%
2.1428571432620
 
0.8%
2.1252530
 
0.7%
2.6666666671892
 
0.5%
Other values (1624)293173
84.4%
ValueCountFrequency (%)
1366
0.1%
1.03448275937
 
< 0.1%
1.03703703741
 
< 0.1%
1.04166666737
 
< 0.1%
1.04347826130
 
< 0.1%
1.05555555626
 
< 0.1%
1.06666666724
 
< 0.1%
1.07142857156
 
< 0.1%
1.07407407441
 
< 0.1%
1.08333333321
 
< 0.1%
ValueCountFrequency (%)
39985
2.9%
2.989795918120
 
< 0.1%
2.98611111192
 
< 0.1%
2.98571428689
 
< 0.1%
2.98484848582
 
< 0.1%
2.98387096876
 
< 0.1%
2.982905983147
 
< 0.1%
2.98181818275
 
< 0.1%
2.981481481146
 
< 0.1%
2.97959183760
 
< 0.1%

count_floors_pre_eq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.130578555
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:41.303380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72776061
Coefficient of variation (CV)0.3415788675
Kurtosis2.36001261
Mean2.130578555
Median Absolute Deviation (MAD)0
Skewness0.8418180575
Sum740310
Variance0.5296355054
MonotonicityNot monotonic
2022-04-30T13:05:41.378042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2209029
60.2%
374171
 
21.3%
153705
 
15.5%
47186
 
2.1%
53039
 
0.9%
6283
 
0.1%
752
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
153705
 
15.5%
2209029
60.2%
374171
 
21.3%
47186
 
2.1%
53039
 
0.9%
6283
 
0.1%
752
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
83
 
< 0.1%
752
 
< 0.1%
6283
 
0.1%
53039
 
0.9%
47186
 
2.1%
374171
 
21.3%
2209029
60.2%
153705
 
15.5%

age
Real number (ℝ≥0)

ZEROS

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.53881353
Minimum0
Maximum995
Zeros34725
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:41.481928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median15
Q330
95-th percentile60
Maximum995
Range995
Interquartile range (IQR)20

Descriptive statistics

Standard deviation73.52774868
Coefficient of variation (CV)2.770574072
Kurtosis157.3751623
Mean26.53881353
Median Absolute Deviation (MAD)10
Skewness12.19598992
Sum9221415
Variance5406.329825
MonotonicityNot monotonic
2022-04-30T13:05:41.594459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1051680
14.9%
1548074
13.8%
545045
13.0%
2042792
12.3%
034725
10.0%
2532586
9.4%
3023977
6.9%
3514420
 
4.2%
4014050
 
4.0%
509619
 
2.8%
Other values (32)30501
8.8%
ValueCountFrequency (%)
034725
10.0%
545045
13.0%
1051680
14.9%
1548074
13.8%
2042792
12.3%
2532586
9.4%
3023977
6.9%
3514420
 
4.2%
4014050
 
4.0%
456255
 
1.8%
ValueCountFrequency (%)
9951851
0.5%
200140
 
< 0.1%
1952
 
< 0.1%
1905
 
< 0.1%
1851
 
< 0.1%
18011
 
< 0.1%
1755
 
< 0.1%
1707
 
< 0.1%
1652
 
< 0.1%
1608
 
< 0.1%

area_percentage
Real number (ℝ≥0)

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.017014467
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:41.723692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q39
95-th percentile16
Maximum100
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.388646483
Coefficient of variation (CV)0.5474165602
Kurtosis30.64344074
Mean8.017014467
Median Absolute Deviation (MAD)2
Skewness3.53162645
Sum2785664
Variance19.26021795
MonotonicityNot monotonic
2022-04-30T13:05:41.848121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
655959
16.1%
749140
14.1%
543556
12.5%
837988
10.9%
929572
8.5%
425675
7.4%
1021030
 
6.1%
1118390
 
5.3%
315687
 
4.5%
1210148
 
2.9%
Other values (76)40324
11.6%
ValueCountFrequency (%)
1125
 
< 0.1%
24275
 
1.2%
315687
 
4.5%
425675
7.4%
543556
12.5%
655959
16.1%
749140
14.1%
837988
10.9%
929572
8.5%
1021030
 
6.1%
ValueCountFrequency (%)
1001
 
< 0.1%
963
< 0.1%
923
< 0.1%
901
 
< 0.1%
867
< 0.1%
855
< 0.1%
843
< 0.1%
834
< 0.1%
821
 
< 0.1%
811
 
< 0.1%

height_percentage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4347985
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:41.960937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile9
Maximum32
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.915555029
Coefficient of variation (CV)0.3524610948
Kurtosis13.53489828
Mean5.4347985
Median Absolute Deviation (MAD)1
Skewness1.762884329
Sum1888424
Variance3.669351069
MonotonicityNot monotonic
2022-04-30T13:05:42.059391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5104869
30.2%
661837
17.8%
450427
14.5%
747360
13.6%
334535
 
9.9%
818460
 
5.3%
212348
 
3.6%
97146
 
2.1%
105934
 
1.7%
121246
 
0.4%
Other values (19)3307
 
1.0%
ValueCountFrequency (%)
212348
 
3.6%
334535
 
9.9%
450427
14.5%
5104869
30.2%
661837
17.8%
747360
13.6%
818460
 
5.3%
97146
 
2.1%
105934
 
1.7%
111242
 
0.4%
ValueCountFrequency (%)
3290
< 0.1%
312
 
< 0.1%
291
 
< 0.1%
282
 
< 0.1%
263
 
< 0.1%
254
 
< 0.1%
246
 
< 0.1%
2312
 
< 0.1%
223
 
< 0.1%
2121
 
< 0.1%

land_surface_condition_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
1
288937 
0
58532 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1288937
83.2%
058532
 
16.8%

Length

2022-04-30T13:05:42.162566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:42.225501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1288937
83.2%
058532
 
16.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

land_surface_condition_2
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
336350 
1
 
11119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0336350
96.8%
111119
 
3.2%

Length

2022-04-30T13:05:42.286371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:42.349057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0336350
96.8%
111119
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

land_surface_condition_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
300056 
1
47413 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0300056
86.4%
147413
 
13.6%

Length

2022-04-30T13:05:42.409498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:42.471633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0300056
86.4%
147413
 
13.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

foundation_type
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2.32957261993832
292374 
1.810953829871676
 
20048
1.8836605890603086
 
18908
1.4533509783533416
 
14182
2.1056629834254146
 
1957

Length

Max length18
Median length16
Mean length16.25942458
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.32957261993832
2nd row2.32957261993832
3rd row2.32957261993832
4th row2.32957261993832
5th row2.32957261993832

Common Values

ValueCountFrequency (%)
2.32957261993832292374
84.1%
1.81095382987167620048
 
5.8%
1.883660589060308618908
 
5.4%
1.453350978353341614182
 
4.1%
2.10566298342541461957
 
0.6%

Length

2022-04-30T13:05:42.538576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:42.613427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.32957261993832292374
84.1%
1.81095382987167620048
 
5.8%
1.883660589060308618908
 
5.4%
1.453350978353341614182
 
4.1%
2.10566298342541461957
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

roof_type_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
1
243975 
0
103494 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1243975
70.2%
0103494
29.8%

Length

2022-04-30T13:05:42.693230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:42.756359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1243975
70.2%
0103494
29.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

roof_type_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
265564 
1
81905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0265564
76.4%
181905
 
23.6%

Length

2022-04-30T13:05:42.832263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:42.902892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0265564
76.4%
181905
 
23.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

roof_type_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
325880 
1
 
21589

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0325880
93.8%
121589
 
6.2%

Length

2022-04-30T13:05:43.092737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:43.149801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0325880
93.8%
121589
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ground_floor_type
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2.309103659496515
279591 
2.25071351047152
33109 
1.634204855040052
32731 
2.0737051792828685
 
1334
1.970472440944882
 
704

Length

Max length18
Median length17
Mean length16.90855299
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.309103659496515
2nd row2.25071351047152
3rd row2.309103659496515
4th row2.309103659496515
5th row2.309103659496515

Common Values

ValueCountFrequency (%)
2.309103659496515279591
80.5%
2.2507135104715233109
 
9.5%
1.63420485504005232731
 
9.4%
2.07370517928286851334
 
0.4%
1.970472440944882704
 
0.2%

Length

2022-04-30T13:05:43.215440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:43.290382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.309103659496515279591
80.5%
2.2507135104715233109
 
9.5%
1.63420485504005232731
 
9.4%
2.07370517928286851334
 
0.4%
1.970472440944882704
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

other_floor_type_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
1
220286 
0
127183 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1220286
63.4%
0127183
36.6%

Length

2022-04-30T13:05:43.369143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:43.431333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1220286
63.4%
0127183
36.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

other_floor_type_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
289330 
1
58139 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0289330
83.3%
158139
 
16.7%

Length

2022-04-30T13:05:43.493012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:43.555459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0289330
83.3%
158139
 
16.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

other_floor_type_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
294557 
1
52912 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0294557
84.8%
152912
 
15.2%

Length

2022-04-30T13:05:43.616145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:43.678485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0294557
84.8%
152912
 
15.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

other_floor_type_4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
331337 
1
 
16132

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0331337
95.4%
116132
 
4.6%

Length

2022-04-30T13:05:43.738760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:43.800638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0331337
95.4%
116132
 
4.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

position_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
290211 
1
57258 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0290211
83.5%
157258
 
16.5%

Length

2022-04-30T13:05:43.862192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:43.925274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0290211
83.5%
157258
 
16.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

position_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
1
269463 
0
78006 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1269463
77.6%
078006
 
22.4%

Length

2022-04-30T13:05:43.986514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:44.047581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1269463
77.6%
078006
 
22.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

position_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
329822 
1
 
17647

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0329822
94.9%
117647
 
5.1%

Length

2022-04-30T13:05:44.103622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:44.161307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0329822
94.9%
117647
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

position_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
344368 
1
 
3101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0344368
99.1%
13101
 
0.9%

Length

2022-04-30T13:05:44.216672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:44.278192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0344368
99.1%
13101
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

plan_configuration
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.238217454
Minimum1.836923077
Maximum2.272727273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:44.333129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.836923077
5-th percentile2.24364583
Q12.24364583
median2.24364583
Q32.24364583
95-th percentile2.24364583
Maximum2.272727273
Range0.4358041958
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04462032701
Coefficient of variation (CV)0.01993565322
Kurtosis51.37833059
Mean2.238217454
Median Absolute Deviation (MAD)0
Skewness-7.219765384
Sum777711.1806
Variance0.001990973582
MonotonicityNot monotonic
2022-04-30T13:05:44.422189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2.24364583333327
95.9%
2.2714335917641
 
2.2%
1.918333794909
 
1.4%
1.836923077450
 
0.1%
2.049132948449
 
0.1%
1.853174603353
 
0.1%
1.893081761195
 
0.1%
1.91304347864
 
< 0.1%
2.15789473754
 
< 0.1%
2.27272727327
 
< 0.1%
ValueCountFrequency (%)
1.836923077450
 
0.1%
1.853174603353
 
0.1%
1.893081761195
 
0.1%
1.91304347864
 
< 0.1%
1.918333794909
 
1.4%
2.049132948449
 
0.1%
2.15789473754
 
< 0.1%
2.24364583333327
95.9%
2.2714335917641
 
2.2%
2.27272727327
 
< 0.1%
ValueCountFrequency (%)
2.27272727327
 
< 0.1%
2.2714335917641
 
2.2%
2.24364583333327
95.9%
2.15789473754
 
< 0.1%
2.049132948449
 
0.1%
1.918333794909
 
1.4%
1.91304347864
 
< 0.1%
1.893081761195
 
0.1%
1.853174603353
 
0.1%
1.836923077450
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
316554 
1
 
30915

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0316554
91.1%
130915
 
8.9%

Length

2022-04-30T13:05:44.522215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:44.587344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0316554
91.1%
130915
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_mud_mortar_stone
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
1
264798 
0
82671 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1264798
76.2%
082671
 
23.8%

Length

2022-04-30T13:05:44.649775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:44.714397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1264798
76.2%
082671
 
23.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
335528 
1
 
11941

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0335528
96.6%
111941
 
3.4%

Length

2022-04-30T13:05:44.775689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:44.969800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0335528
96.6%
111941
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
341104 
1
 
6365

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0341104
98.2%
16365
 
1.8%

Length

2022-04-30T13:05:45.022590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:45.082907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0341104
98.2%
16365
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_mud_mortar_brick
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
323848 
1
 
23621

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0323848
93.2%
123621
 
6.8%

Length

2022-04-30T13:05:45.143404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:45.206662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0323848
93.2%
123621
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_cement_mortar_brick
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
321440 
1
 
26029

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0321440
92.5%
126029
 
7.5%

Length

2022-04-30T13:05:45.268369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:45.331194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0321440
92.5%
126029
 
7.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_timber
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
258995 
1
88474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0258995
74.5%
188474
 
25.5%

Length

2022-04-30T13:05:45.392604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:45.456709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0258995
74.5%
188474
 
25.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_bamboo
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
318046 
1
 
29423

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0318046
91.5%
129423
 
8.5%

Length

2022-04-30T13:05:45.518101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:45.587543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0318046
91.5%
129423
 
8.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_rc_non_engineered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
332678 
1
 
14791

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0332678
95.7%
114791
 
4.3%

Length

2022-04-30T13:05:45.651302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:45.715951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0332678
95.7%
114791
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_superstructure_rc_engineered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
341964 
1
 
5505

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0341964
98.4%
15505
 
1.6%

Length

2022-04-30T13:05:45.778065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:45.841581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0341964
98.4%
15505
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
342243 
1
 
5226

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0342243
98.5%
15226
 
1.5%

Length

2022-04-30T13:05:45.903026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:45.968364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0342243
98.5%
15226
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2.2440632982517665
334633 
1.8940493468795356
 
7307
2.4157639148300336
 
3539
2.2172437202987103
 
1990

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.2440632982517665
2nd row2.2440632982517665
3rd row2.2440632982517665
4th row2.2440632982517665
5th row2.2440632982517665

Common Values

ValueCountFrequency (%)
2.2440632982517665334633
96.3%
1.89404934687953567307
 
2.1%
2.41576391483003363539
 
1.0%
2.21724372029871031990
 
0.6%

Length

2022-04-30T13:05:46.030283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:46.097141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.2440632982517665334633
96.3%
1.89404934687953567307
 
2.1%
2.41576391483003363539
 
1.0%
2.21724372029871031990
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

count_families
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9837395566
Minimum0
Maximum9
Zeros27937
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2022-04-30T13:05:46.166296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4193854935
Coefficient of variation (CV)0.4263176069
Kurtosis17.24872251
Mean0.9837395566
Median Absolute Deviation (MAD)0
Skewness1.627559333
Sum341819
Variance0.1758841922
MonotonicityNot monotonic
2022-04-30T13:05:46.247130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1301377
86.7%
027937
 
8.0%
215010
 
4.3%
32415
 
0.7%
4547
 
0.2%
5135
 
< 0.1%
633
 
< 0.1%
78
 
< 0.1%
94
 
< 0.1%
83
 
< 0.1%
ValueCountFrequency (%)
027937
 
8.0%
1301377
86.7%
215010
 
4.3%
32415
 
0.7%
4547
 
0.2%
5135
 
< 0.1%
633
 
< 0.1%
78
 
< 0.1%
83
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
94
 
< 0.1%
83
 
< 0.1%
78
 
< 0.1%
633
 
< 0.1%
5135
 
< 0.1%
4547
 
0.2%
32415
 
0.7%
215010
 
4.3%
1301377
86.7%
027937
 
8.0%

has_secondary_use
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
308630 
1
38839 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0308630
88.8%
138839
 
11.2%

Length

2022-04-30T13:05:46.338405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:46.402050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0308630
88.8%
138839
 
11.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_secondary_use_agriculture
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
325124 
1
 
22345

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0325124
93.6%
122345
 
6.4%

Length

2022-04-30T13:05:46.463141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:46.526121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0325124
93.6%
122345
 
6.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_secondary_use_hotel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
335764 
1
 
11705

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0335764
96.6%
111705
 
3.4%

Length

2022-04-30T13:05:46.587181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:46.650213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0335764
96.6%
111705
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
344642 
1
 
2827

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0344642
99.2%
12827
 
0.8%

Length

2022-04-30T13:05:46.836458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:46.896818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0344642
99.2%
12827
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
347136 
1
 
333

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0347136
99.9%
1333
 
0.1%

Length

2022-04-30T13:05:46.956461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:47.018512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0347136
99.9%
1333
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
347343 
1
 
126

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0347343
> 99.9%
1126
 
< 0.1%

Length

2022-04-30T13:05:47.079241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:47.141428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0347343
> 99.9%
1126
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
347103 
1
 
366

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0347103
99.9%
1366
 
0.1%

Length

2022-04-30T13:05:47.202666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:47.266608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0347103
99.9%
1366
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
347411 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0347411
> 99.9%
158
 
< 0.1%

Length

2022-04-30T13:05:47.327536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:47.391488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0347411
> 99.9%
158
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
347421 
1
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0347421
> 99.9%
148
 
< 0.1%

Length

2022-04-30T13:05:47.452499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:47.518850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0347421
> 99.9%
148
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
347442 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0347442
> 99.9%
127
 
< 0.1%

Length

2022-04-30T13:05:47.582093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:47.645953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0347442
> 99.9%
127
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
0
345709 
1
 
1760

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0345709
99.5%
11760
 
0.5%

Length

2022-04-30T13:05:47.707315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-30T13:05:47.770892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0345709
99.5%
11760
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-30T13:05:34.370976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:16.156141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:18.259161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:20.138156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:22.652187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:24.569384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:26.687641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:28.661171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:30.600991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:32.469015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:34.586649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:16.434565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:18.464335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:21.033270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:22.828713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:24.747465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:26.908062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:28.846573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:30.794540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:32.663783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:34.799237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:16.641182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:18.666685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:21.198302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:23.007381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:24.923064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:27.120581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:29.030309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:30.986653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:32.844769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:35.020392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:16.856023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:18.879140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:21.390579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:23.194878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:25.110290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:27.336746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:29.222485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:31.188098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:33.086884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:35.236223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:17.073314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:19.086993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:21.585553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:23.396474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:25.440699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:27.550592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:29.413459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:31.380115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:33.278681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:35.612234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:17.276018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:19.273823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:21.780629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:23.595544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:25.645056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:27.753107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:29.604995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:31.569578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:33.470377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:35.798027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:17.469609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:19.448565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:21.954282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:23.788411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:25.880577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:27.935441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:29.780543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:31.748624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:33.650311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:35.973279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:17.669721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:19.625058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:22.131505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:23.990747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:26.100589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:28.112770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:29.959475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:31.926479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:33.830144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:36.154266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:17.863836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:19.804074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:22.306442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:24.194806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:26.294564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:28.307567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:30.140879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:32.115573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:34.008456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:36.335365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:18.058915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:19.972991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:22.478079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:24.389196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:26.486856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:28.482378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:30.432636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:32.293246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-30T13:05:34.183114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-30T13:05:47.888695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-30T13:05:48.302758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-30T13:05:48.721584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-30T13:05:49.245495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-30T13:05:49.601954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-30T13:05:36.610215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-30T13:05:38.425527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

building_idgeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_condition_1land_surface_condition_2land_surface_condition_3foundation_typeroof_type_1roof_type_2roof_type_3ground_floor_typeother_floor_type_1other_floor_type_2other_floor_type_3other_floor_type_4position_1position_2position_3position_4plan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_other
08029062.1617242.7407412.837838230651002.3295731002.309104100010002.243646110000000002.244063100000000000
1288302.4852732.4874372.062500210870102.3295731002.250714100001002.243646010000000002.244063100000000000
2949472.5633692.5187502.580882210551002.3295731002.309104010010002.243646010000000002.244063100000000000
35908822.0009602.1073172.096774210651002.3295731002.309104010001002.243646010000110002.244063100000000000
42019442.3377132.3487482.368852330891002.3295731002.309104010001002.243646100000000002.244063100000000000
53330202.4852732.5465392.368421210951002.3295731002.309104100001002.243646010000000002.244063111000000000
67284512.0260232.1376812.555556225340012.3295731002.250714100001002.243646010000000002.244063100000000000
74755151.9264641.2828691.10000020861001.8109540101.634205010001001.918334000001100002.244063100000000000
84411262.0653202.1050852.189189215861002.3295730102.309104100001002.243646010000100002.244063100000000000
99895001.7308871.5451091.551402101341001.4533511001.634205001001002.243646000001000002.244063100000000000

Last rows

building_idgeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_condition_1land_surface_condition_2land_surface_condition_3foundation_typeroof_type_1roof_type_2roof_type_3ground_floor_typeother_floor_type_1other_floor_type_2other_floor_type_3other_floor_type_4position_1position_2position_3position_4plan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_other
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3474616986121.9264641.4330541.523810210951001.8109540102.309104010001001.918334000000100002.244063100000000000
3474624451922.1617241.9913541.868852251461002.3295730012.250714000101002.243646000000001002.244063100000000000
3474636401152.2977262.0749192.071713251651001.4533510011.634205000101002.243646000000000102.244063210100000000
3474643100282.1621362.1226421.8611113702061002.3295730102.309104100010002.243646010000100002.415764111000000000
3474656635672.3419542.3371762.722222325670012.3295731002.309104100001002.243646111000000002.244063100000000000
34746610491602.0009602.0197041.909091150331002.3295731002.309104001001002.243646010000100002.244063100000000000
3474674427852.1617242.2959502.13636425951002.3295731002.309104100001002.243646110000000001.894049100000000000
3474685013721.7308871.4339181.5142862101141002.3295730101.634205100001002.243646000001000002.244063100000000000